{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Implementing a Neural Network\n",
    "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# A bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Create a small net and some toy data to check your implementations.\n",
    "# Note that we set the random seed for repeatable experiments.\n",
    "\n",
    "input_size = 4\n",
    "hidden_size = 10\n",
    "num_classes = 3\n",
    "num_inputs = 5\n",
    "\n",
    "def init_toy_model():\n",
    "    np.random.seed(0)\n",
    "    return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n",
    "\n",
    "def init_toy_data():\n",
    "    np.random.seed(1)\n",
    "    X = 10 * np.random.randn(num_inputs, input_size)\n",
    "    y = np.array([0, 1, 2, 2, 1])\n",
    "    return X, y\n",
    "\n",
    "net = init_toy_model()\n",
    "X, y = init_toy_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute scores\n",
    "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n",
    "\n",
    "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "correct scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "Difference between your scores and correct scores:\n",
      "3.6802720745909845e-08\n"
     ]
    }
   ],
   "source": [
    "scores = net.loss(X)\n",
    "print('Your scores:')\n",
    "print(scores)\n",
    "print()\n",
    "print('correct scores:')\n",
    "correct_scores = np.asarray([\n",
    "  [-0.81233741, -1.27654624, -0.70335995],\n",
    "  [-0.17129677, -1.18803311, -0.47310444],\n",
    "  [-0.51590475, -1.01354314, -0.8504215 ],\n",
    "  [-0.15419291, -0.48629638, -0.52901952],\n",
    "  [-0.00618733, -0.12435261, -0.15226949]])\n",
    "print(correct_scores)\n",
    "print()\n",
    "\n",
    "# The difference should be very small. We get < 1e-7\n",
    "print('Difference between your scores and correct scores:')\n",
    "print(np.sum(np.abs(scores - correct_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute loss\n",
    "In the same function, implement the second part that computes the data and regularization loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference between your loss and correct loss:\n",
      "1.7985612998927536e-13\n"
     ]
    }
   ],
   "source": [
    "loss, _ = net.loss(X, y, reg=0.05)\n",
    "correct_loss = 1.30378789133\n",
    "\n",
    "# should be very small, we get < 1e-12\n",
    "print('Difference between your loss and correct loss:')\n",
    "print(np.sum(np.abs(loss - correct_loss)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward pass\n",
    "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 3.561318e-09\n",
      "b1 max relative error: 2.738421e-09\n",
      "W2 max relative error: 3.440708e-09\n",
      "b2 max relative error: 4.447646e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.gradient_check import eval_numerical_gradient\n",
    "\n",
    "# Use numeric gradient checking to check your implementation of the backward pass.\n",
    "# If your implementation is correct, the difference between the numeric and\n",
    "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n",
    "\n",
    "loss, grads = net.loss(X, y, reg=0.05)\n",
    "\n",
    "# these should all be less than 1e-8 or so\n",
    "for param_name in grads:\n",
    "    f = lambda W: net.loss(X, y, reg=0.05)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n",
    "\n",
    "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training loss:  0.017149607938732048\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = init_toy_model()\n",
    "stats = net.train(X, y, X, y,\n",
    "            learning_rate=1e-1, reg=5e-6,\n",
    "            num_iters=100, verbose=False)\n",
    "\n",
    "print('Final training loss: ', stats['loss_history'][-1])\n",
    "\n",
    "# plot the loss history\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('Training Loss history')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data\n",
    "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3072)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    \n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "        del X_train, y_train\n",
    "        del X_test, y_test\n",
    "        print('Clear previously loaded data.')\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "        \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    # Reshape data to rows\n",
    "    X_train = X_train.reshape(num_training, -1)\n",
    "    X_val = X_val.reshape(num_validation, -1)\n",
    "    X_test = X_test.reshape(num_test, -1)\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a network\n",
    "To train our network we will use SGD. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302976\n",
      "iteration 100 / 1000: loss 2.302487\n",
      "iteration 200 / 1000: loss 2.298445\n",
      "iteration 300 / 1000: loss 2.272842\n",
      "iteration 400 / 1000: loss 2.160314\n",
      "iteration 500 / 1000: loss 2.144013\n",
      "iteration 600 / 1000: loss 2.070936\n",
      "iteration 700 / 1000: loss 1.976521\n",
      "iteration 800 / 1000: loss 1.951775\n",
      "iteration 900 / 1000: loss 1.958305\n",
      "Validation accuracy:  0.28\n"
     ]
    }
   ],
   "source": [
    "input_size = 32 * 32 * 3\n",
    "hidden_size = 50\n",
    "num_classes = 10\n",
    "net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "\n",
    "# Train the network\n",
    "stats = net.train(X_train, y_train, X_val, y_val,\n",
    "            num_iters=1000, batch_size=200,\n",
    "            learning_rate=1e-4, learning_rate_decay=0.95,\n",
    "            reg=0.25, verbose=True)\n",
    "\n",
    "# Predict on the validation set\n",
    "val_acc = (net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debug the training\n",
    "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n",
    "\n",
    "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n",
    "\n",
    "Another strategy is to visualize the weights that were learned in the first layer of the network. **In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss function and train / validation accuracies\n",
    "fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(8, 8))\n",
    "\n",
    "ax1.plot(stats['loss_history'])\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "ax2.plot(stats['train_acc_history'], label='train')\n",
    "plt.plot(stats['val_acc_history'], label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Classification accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "# Visualize the weights of the network\n",
    "\n",
    "def show_net_weights(net):\n",
    "    W1 = net.params['W1']\n",
    "    W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n",
    "    plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_net_weights(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune your hyperparameters\n",
    "\n",
    "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, **which seems to suggest that the learning rate may be too low.** Moreover, there is **no gap between the training and validation accuracy**, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n",
    "\n",
    "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n",
    "\n",
    "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n",
    "\n",
    "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can (52% could serve as a reference), with a fully-connected Neural Network. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Explain your hyperparameter tuning process below.**\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [],
   "source": [
    "best_net = None # store the best model into this \n",
    "\n",
    "#################################################################################\n",
    "# TODO: Tune hyperparameters using the validation set. Store your best trained  #\n",
    "# model in best_net.                                                            #\n",
    "#                                                                               #\n",
    "# To help debug your network, it may help to use visualizations similar to the  #\n",
    "# ones we used above; these visualizations will have significant qualitative    #\n",
    "# differences from the ones we saw above for the poorly tuned network.          #\n",
    "#                                                                               #\n",
    "# Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n",
    "# write code to sweep through possible combinations of hyperparameters          #\n",
    "# automatically like we did on the previous exercises.                          #\n",
    "#################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize the weights of the best network\n",
    "show_net_weights(best_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run on the test set\n",
    "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_acc = (best_net.predict(X_test) == y_test).mean()\n",
    "print('Test accuracy: ', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question**\n",
    "\n",
    "Now that you have trained a Neural Network classifier, you may find that your testing accuracy is much lower than the training accuracy. In what ways can we decrease this gap? Select all that apply.\n",
    "\n",
    "1. Train on a larger dataset.\n",
    "2. Add more hidden units.\n",
    "3. Increase the regularization strength.\n",
    "4. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ 1, 2, 3\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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